Machine Learning Engineer
$160K–$200K+ Offers For Graduates $160K–$200K+ Offers For Graduates 

3 weeks remote, 7 weeks onsite in Austin, TX
80–100 hours/week for 10 weeks
In-person
Short-term contract
full-time (90 hrs/week)

Machine Learning Engineer   $160K–$200K+ Offers For Graduates $160K–$200K+ Offers For Graduates 

Description

Many engineers claim they want to build systems that count. This fellowship gives you the opportunity to demonstrate it: continuous delivery, rigorous assessment, and operational AI infrastructure that directly influences how the federal government functions. No résumé posturing. No academic abstractions. Only weekly production releases, executed under real constraints.

Gauntlet for America is a fully funded, competitive 10-week engineering fellowship created to develop AI-native technical capability for United States government operations. It serves as a high-pressure evaluation environment for seasoned engineers prepared to show they can design and deploy production-tier AI systems where security, reliability, and tangible impact are non-negotiable.

Participants deliver weekly outputs, perform under continuous evaluation, and collaborate with other elite engineers. After successful completion, fellows transition into federal GS-12 engineering positions (~$150K + comprehensive federal benefits), contributing to systems that directly determine how government technology operates.

The fellowship spans 10 weeks: 3 weeks conducted remotely, then 7 weeks in person in Austin, Texas. Expect a demanding workload (80–100 hours per week) structured to accelerate learning velocity, generate clear performance signal, and unlock exceptional career trajectories.

Program Outcomes:

  • 10+ production-grade AI systems delivered throughout the fellowship period
  • Guaranteed placement into a federal engineering position (GS-12 tier, ~$160K–$200K+ based on background + full benefits)
  • Contribution to high-consequence systems defining how the U.S. government builds and runs technology
  • Membership in a network of AI-native engineers operating at the leading edge of public sector technology

If you are prepared to be judged by what you ship — not by institutional affiliations — submit your application today.

What you will be doing

  • Deliver production-quality AI applications weekly against firm deadlines
  • Develop systems using modern AI-native tooling (agents, tool integration, evaluation frameworks, retrieval architectures, deployment pipelines)
  • Operate in a high-feedback collaborative and competitive engineering cohort
  • Engage with authentic, unstructured problem domains that mirror government and enterprise contexts
  • Convert operational briefs into scoped, dependable, shippable systems

What you will NOT be doing

  • Attending theoretical classes or passive instruction — all time is devoted to building and deploying
  • Waiting extended periods to see your work reach production — real system deployments happen every week
  • Depending on academic credentials, pedigree, or interview results to secure your role — production output is the sole measure
  • Operating in a risk-free prototype environment — the systems you create must meet actual security and reliability standards

Key responsibilities

Deliver production-quality AI systems under operational constraints that validate readiness for federal engineering positions.

Candidate requirements

  • U.S. citizenship mandatory (no exceptions; background check required)
  • Proven engineering capability (recent graduates and experienced professionals both considered)
  • Available to relocate to Austin, TX for 7 weeks (full-time, onsite participation)
  • Available to relocate to the Washington, DC region after program completion (remote work not available)
  • Exceptional problem-solving skills, rapid learning capacity, and disciplined reasoning under pressure
  • Strong receptiveness to feedback and capability to perform in high-intensity settings

Meet a successful candidate

Watch Interview
Fabiano Lucchese
Fabiano  |  SVP of Software Engineering
Brazil

Does your company encourage your natural creativity? This Brazilian engineering leader rediscovered his purpose after unleashing both his an...

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Applying for a role? Here’s what to expect.

Crossover's skill assessment process combines innovative AI power with decades of human research, to take the guesswork, human bias, and pointless filters out of recruiting high-performing teams.

Chat-style
screening interview.
STEP 1

Chat-style
screening interview.

Cognitive 
aptitude test.
STEP 2

Cognitive 
aptitude test.

Prove real-world 
job skills.
STEP 3

Prove real-world 
job skills.

Interview with the hiring manager.
STEP 4

Interview with the hiring manager.

Pass
proctored test.
STEP 5

Pass
proctored test.

Accept job offer.
STEP 6

Accept job offer.

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What you will learn

Phase 1: Remote Instruction (Weeks 1–3) — AI-Native Engineering Fundamentals

  • AI-native development practices (coding agents, MCP, real-time collaboration tools)
  • Retrieval-Augmented Generation (RAG), embedding techniques, and vector database systems
  • Accelerated project cycles emphasizing delivery under tight constraints

Phase 2: Austin Onsite Program (Weeks 4–10) — Production AI at Enterprise Scale

  • Agent architectures, evaluation systems, verification frameworks, and observability tooling (LangChain/LangSmith/LangFuse/CrewAI)
  • Enterprise-level delivery practices: quality assurance, system reliability, and rigorous execution standards
  • Model fine-tuning + deployment strategies (LoRA/QLoRA + production integration workflows)
  • Multi-agent modernization approaches for legacy production codebases
  • Multimodal AI development (image/video/voice processing) and scalable cloud infrastructure (AWS/Azure)

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Premium pay for premium talent

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